
We characterise research efforts in the nascent Novel Class Discovery setting into single-stage and two-stage methods, based on their data requirement during training. We further propose a simple yet effective method which enhances both these settings by enforcing separability in the latent representations. Our experimental analysis on multiple settings on two benchmark datasets corroborates with our assertions. Advancements in NCD can help continual learning models to operate in an open-world [1]}, [2]}, where it can automatically identify novel categories and then incrementally learn them. We hope this pragmatic setting would be extensively explored in follow-up works.
